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Can the no of test data be more than training data?
You should test on as much data as possible. Normally this comes as a tradeoff when splitting between training and testing data (more test data means less training data, which is arguably more important), but in this case you are purposefully reducing the training set size to analyze the effect of that reduction.
Can test accuracy be higher than training?
2 Answers. Test accuracy should not be higher than train since the model is optimized for the latter. You should do a proper train/test split in which both of them have the same underlying distribution. Most likely you provided a completely different (and more agreeable) dataset for test.
Why training data set must be larger than testing data set?
Larger test datasets ensure a more accurate calculation of model performance. Training on smaller datasets can be done by sampling techniques such as stratified sampling. It will speed up your training (because you use less data) and make your results more reliable.
When to use test data vs training data?
On the other hand, the test set is just a formalism used to estimate how good the model is. You cannot know for sure how accurate your model it is going to be with future credit applications, but what you can do is to save a small part of your training data, and use it only to check the model’s performance after it has been built.
Can a test error be higher than a training error?
You description is confusing, but it is totally possible to have test error both lower and higher than training error. A lower training error is expected when a method easily overfits to the training data, yet, poorly generalizes.
How can classification test accuracy be higher than training?
Make sure the reported “test accuracy” comes from independent data (double/nested cross validation): if your program does data driven optimization (e.g. choosing the “best” features by comparing many models), this is more like at training error (goodness of fit) than like a generalization error.
How is Mae used in machine learning ( ML )?
In Machine Learning, MAE is a model evaluation metric often used with regression models. For a worked example of MAE calculation, do check my next article on Mean Absolute Error (MAE) ~ Sample Calculation. Also in coming articles I will give an explanation of other metrics for verifying accuracy of our model such as Root mean squared error (RMSE).